A novel deep learning system for STEMI prognostic prediction from multi-sequence cardiac magnetic resonance

ST-elevation myocardial infarction (STEMI) remains a leading cause of cardiovascular morbidity and mortality worldwide, and accurate early risk stratification is critical for implementing precision therapies in clinical practice. However, existing clinical risk scores and manually derived imaging biomarkers have limited accuracy in predicting post-STEMI outcomes. To address this gap, we developed DeepSTEMI, an end-to-end deep learning system that integrates multi-sequence cardiac magnetic resonance (CMR) images with clinical parameters for predicting 2-year major adverse cardiovascular events (MACE). The system comprised two key algorithmic modules: a U-Net module that automatically segments heart regions from raw CMR images and a Transformer-based module that predicted future cardiovascular events. DeepSTEMI was developed using a multicenter dataset (n = 610; 20,618 images) from STEMI patients enrolled in the EARLY-MYO-CMR registry (NCT03768453), with external validation performed in 334 patients (9944 images) from three independent cardiac centers. In external validation, DeepSTEMI demonstrated superior predictive performance compared to conventional clinical risk scores and manual CMR parameters (AUC 0.894, 95% CI: 0.823-0.965; overall accuracy 94.3%). The model identified high-risk patients who exhibited a 20-fold MACE risk compared to low-risk counterparts (HR 20.43, log-rank P < 0.001). SHapley Additive exPlanations (SHAP) analysis revealed that DeepSTEMI's predictive power stems from clinical-imaging synergy, enabling it to capture complex pathological patterns. DeepSTEMI achieved consistently superior performance over the Eitel score across all subgroups, with the greatest benefit observed in women (NRI 1.597) and in patients imaged 4-7 d post-STEMI (NRI 1.442). Overall, DeepSTEMI serves as an automated, scalable, and interpretable clinical copilot, which advances post-STEMI risk stratification beyond the limitations of current paradigms.